Model driven optimization of annotator execution in question answering system

ABSTRACT

Mechanisms are provided for scheduling execution of pre-execution operations of an annotator of a question and answer (QA) system pipeline. A model is used to represent a system of annotators of the QA system pipeline, where the model represents each annotator as a node having one or more performance parameters indicating a performance of an execution of an annotator corresponding to the node. For each annotator in a set of annotators of the system of annotators, an effective response time for the annotator is calculated based on the performance parameters. A pre-execution start interval for a first annotator based on an effective response time of a second annotator is calculated where execution of the first annotator is sequentially after execution of the second annotator. Execution of pre-execution operations associated with the first annotator is scheduled based on the calculated pre-execution start interval for the first annotator.

This application is a continuation of application Ser. No. 14/252,452,filed Apr. 14, 2014, status pending.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for performingmodel driven optimization of annotator execution in a question answeringsystem.

With the increased usage of computing networks, such as the Internet,humans are currently inundated and overwhelmed with the amount ofinformation available to them from various structured and unstructuredsources. However, information gaps abound as users try to piece togetherwhat they can find that they believe to be relevant during searches forinformation on various subjects. To assist with such searches, recentresearch has been directed to generating Question and Answer (QA)systems which may take an input question, analyze it, and return resultsindicative of the most probable answer to the input question. QA systemsprovide automated mechanisms for searching through large sets of sourcesof content, e.g., electronic documents, and analyze them with regard toan input question to determine an answer to the question and aconfidence measure as to how accurate an answer is for answering theinput question.

One such QA system is the Watson™ system available from InternationalBusiness Machines (IBM) Corporation of Armonk, N.Y. The Watson™ systemis an application of advanced natural language processing, informationretrieval, knowledge representation and reasoning, and machine learningtechnologies to the field of open domain question answering. The Watson™system is built on IBM's DeepQA™ technology used for hypothesisgeneration, massive evidence gathering, analysis, and scoring. DeepQA™takes an input question, analyzes it, decomposes the question intoconstituent parts, generates one or more hypothesis based on thedecomposed question and results of a primary search of answer sources,performs hypothesis and evidence scoring based on a retrieval ofevidence from evidence sources, performs synthesis of the one or morehypothesis, and based on trained models, performs a final merging andranking to output an answer to the input question along with aconfidence measure.

Various United States Patent Application Publications describe varioustypes of question and answer systems. U.S. Patent ApplicationPublication No. 2011/0125734 discloses a mechanism for generatingquestion and answer pairs based on a corpus of data. The system startswith a set of questions and then analyzes the set of content to extractanswer to those questions. U.S. Patent Application Publication No.2011/0066587 discloses a mechanism for converting a report of analyzedinformation into a collection of questions and determining whetheranswers for the collection of questions are answered or refuted from theinformation set. The results data are incorporated into an updatedinformation model.

SUMMARY

In one illustrative embodiment, a method, in a data processing systemcomprising a processor and a memory, is provided for schedulingexecution of pre-execution operations of an annotator of a question andanswer (QA) system pipeline. The method comprises using, by the dataprocessing system, a model to represent a system of annotators of the QAsystem pipeline. The model represents each annotator in a set ofannotators of the system of annotators as a node having one or moreperformance parameters for indicating a performance of an execution ofan annotator corresponding to the node. The method further comprisesdetermining, by the data processing system, for each annotator in thesystem of annotators, an effective response time for the annotator basedon the one or more performance parameters. Moreover, the methodcomprises calculating, by the data processing system, a pre-executionstart interval for a first annotator based on an effective response timeof a second annotator. Execution of the first annotator is sequentiallyafter execution of the second annotator. In addition, the methodcomprises scheduling, by the data processing system, execution ofpre-execution operations associated with the first annotator based onthe calculated pre-execution start interval for the first annotator.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer creation (QA) system in a computer network;

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments may be implemented;

FIG. 3 illustrates a QA system pipeline for processing an input questionin accordance with one illustrative embodiment;

FIG. 4A illustrates one example of a tandem open queuing network modelof a system of annotators in accordance with one illustrativeembodiment;

FIG. 4B illustrates one example of a tandem open queue model of asubsystem in accordance with one illustrative embodiment;

FIG. 4C illustrates one example of a single equivalent queuerepresentation for the tandem open queue model of FIG. 4B in accordancewith one illustrative embodiment;

FIG. 4D illustrates one example of a model of a system as a discretetime Markov chain (DTMC) model in which the states of the DTMC areindividual subsystems and the transitions between the states, orsubsystems, are represented by the branching probabilities in accordancewith one illustrative embodiment;

FIG. 5 is a flowchart outlining an example operation for determining theeffective response time and pre-execution start interval forannotators/aggregates in accordance with one illustrative embodiment;and

FIG. 6 is a flowchart outlining an example operation for scheduling theoperation of annotators/aggregates in accordance with one illustrativeembodiment.

DETAILED DESCRIPTION

In a question and answer (QA) system, such as the Watson™ QA systempreviously mentioned above, numerous annotators are used to analyze theinput questions and the corpus of documents and other data used as abasis for generating answers to the input questions. An annotator is aprogram that takes a portion of input text, extracts structuredinformation from it, and generates annotations, or metadata, that areattached by the annotator to the source/original text data. The term“annotation” refers to the process followed by the annotator and theresulting metadata that provides elements of structure that can bereferenced or acted upon by other programs, annotators, or the like,that read and understand the annotated text data. Annotators maycomprise logic configured in many different ways and for many differentpurposes. In general, annotators may look for particular words,combinations of words, perform context analysis for words, and the like,to identify the types of words in a portion of text, their purposewithin the portion of text, e.g., parts of speech, types of informationsought or being provided, and the like. Annotators may be generic, e.g.,identifying nouns, names of persons, places, dates, etc., or specific toa particular purpose (e.g., identifying medical terms) or domain (e.g.,identifying questions/content directed to pancreatic cancer diagnosisand treatment).

QA systems may be generic in nature or customized for a particulardomain or subset of domains. A “domain” in the context of QA systems andthe present description is a concept area, e.g., sports, financialindustry, medical industry, legal concepts, etc. Domains may be definedat various granularities such that one domain may be a subset of alarger domain. For example, one domain may be “medical”, a sub-domainmay be “cancer”, a sub-sub-domain may be “diagnosis and treatment,” andyet another sub-sub-sub-domain may be “pancreatic cancer.” QA systemsmay be configured to handle questions and provide answers of aparticular domain or set of domains and sub-domains, possibly using acorpus of information that is specifically for that particulardomain/sub-domains, e.g., medical records, journals, publications, etc.In such a case the QA system is trained specifically for the particulardomains/sub-domains that it is intended to handle, including providingthe QA system with annotators that are configured and trained for theparticular domains/sub-domains.

Alternatively, a QA system may be more generic in nature such that it isconfigured to handle questions that may be directed to any domain or alarge group of related and unrelated domains. In such a case, the QAsystem may have many different annotators that are configured to performannotation operations either generically or with regard to a variety ofdifferent domains/sub-domains that may not be related to one another.The annotators may be used as part of a pre-processing operation of acorpus of content or during runtime operation as part of the hypothesisgeneration or other stages of a QA system pipeline when generatingcandidate answers to an input question.

Annotators may be provided as individual annotators or as a group ofannotators treated as a single entity, referred to herein as anannotator “aggregate.” A QA system pipeline, described hereafter, mayhave multiple annotators and/or annotator aggregates runningconcurrently. Moreover, some annotators and annotator aggregates of a QAsystem pipeline may have dependencies upon one another, or upon anotherelement of the QA system pipeline, for providing some information ordata prior to the dependent annotator or annotator aggregate executing.For example, in some embodiments, a downstream annotator/annotatoraggregate (referred to hereafter as simply an “aggregate”) may bedependent upon an upstream process that provides input data to thedownstream annotator/aggregate. In addition, some annotators andaggregates may have various sub-systems associated with them thatperform various preliminary operations prior to the annotators andaggregates operating on the data input to the annotators and aggregates,e.g., loading a corpora into memory so that the annotators/aggregatesmay operate on the corpora, initiating type maps used by theannotators/aggregates when analyzing the content of the loaded corpora,pre-loading certain other data used by the annotator/aggregate whenperforming its functions, or the like.

It has been determined that the operation of a QA system may be improvedby expediting the execution of operations of sub-systems and upstreammechanisms upon which annotators and aggregates are dependent. However,in order to expedite such execution, it is important to know whichsub-systems and upstream mechanisms the annotators and aggregates aredependent upon as well as the proper timing for performing theseoperations. By improving operation of the QA system pipeline byscheduling the performance of these “pre-execution” operations inaccordance with determined dependencies and timing, the concurrentexecution of annotators and aggregates is increased. However, one mustalso optimize the amount of concurrent execution of annotators andaggregates so as to take optimum advantage of the available resourceswithout over-saturating the resources and making performancedeteriorate.

The illustrative embodiments provide mechanisms for utilizing an openqueuing network to approximate and model the response time associatedwith an annotator or aggregate. In this model, the group ofannotators/aggregates of a QA system pipeline are modeled as a tandemqueue in which each annotator/aggregate is a queue node representing aseparate open queue in the tandem queue model. Response times for thevarious nodes in the model are determined and, based on the modeledresponse time, and the configuration of downstreamannotators/aggregates, non-dependent sub-system operations are scheduledso as to optimize the overall response time for a given set of requests.

Thus, with the mechanisms of the illustrative embodiment, each annotatoror aggregate of the QA system pipeline is defined as a sub-system and isdesignated as to whether they have one or more pre-execution operationsthat may be executed with the results of the pre-execution operationsbeing stored/cached for use by the annotator/aggregate. The entire groupof subsystems, i.e. the group of annotators and aggregates, arerepresented as a tandem queue (one queue providing an output to a nextqueue in the tandem queue) with each subsystem being a queue in thetandem queue, however if a subsystem is large enough it may be modeledas a tandem queue in itself.

A model is generated to define and determine the average response timefor each subsystem, referred to hereafter as the “effective responsetime.” The effective response time of a subsystem (annotator/aggregate)in the model is used to determine when the pre-execution operations ofthe next subsystem (annotator/aggregate) in the model can beginexecuting so as to optimize the timing of the execution of thepre-execution operation and the subsequent subsystem. In determiningwhen the pre-execution operations of the next subsystem, a calculationof the current execution time minus the effective response time of theprevious subsystem is set equal to the pre-execution start interval.Thus, for example, if the current execution time of a subsystem, i.e. apoint in model simulation time at which the next subsystem beginsexecution, is 15 seconds from a start point, and a previous subsystem'seffective response time is 10 seconds, then the pre-execution startinterval is 5 seconds (15 seconds minus 10 seconds). This process may beperformed for each subsystem of the model to generate pre-executionstart intervals for each subsystem of the model.

During runtime operation, the pre-execution start intervals may be usedfor scheduling the pre-execution operations of theannotators/aggregates. In executing the pre-execution operations, thepre-execution operation checks for pre-execution input data and startsthe pre-execution operation in response to the pre-execution input databeing available. In some embodiments, pre-execution input data may beplaced in a queue structure from which the pre-execution input data isloaded and annotators/aggregates are executed on demand but not beforethe most optimal pre-execution start interval. Once the pre-executionoperation is executed, the resulting data, referred to as “intermediatedata” herein, is stored/cached for use by the annotator/aggregate whenthe annotator/aggregate is fully executed. The annotator/aggregate isthen executed fully in accordance with its normal operation.

As a result, the execution of the pre-execution operations of theannotators/aggregates is scheduled earlier and can be performed inparallel with other annotator/aggregate operations being performed byother annotators/aggregates. Thus, the overall system ofannotators/aggregates execute faster and use processor/memory as well asother system resources at an optimal time.

However, it should be appreciated that there may conditions under whichthe number of pre-execution operations that may run in parallel mayconsume too many resources, e.g., memory, processor cycles, or the like,and may negatively affect performance. The illustrative embodiments mayimplement concurrency control logic for controlling the level ofconcurrency of the pre-execution operations by controlling whichannotator pre-execution operations are performed in parallel. Forexample, in one illustrative embodiment, the concurrency control mayidentify and execute in parallel the pre-execution operations ofannotators that are some “distance” apart. For example, if annotator #1is associated with annotator #2, e.g., provides an input into annotator#2 or otherwise annotator #2 operates on the output of annotator #1, andannotator #2 is associated with annotator #3, annotator #3 is associatedwith annotator #4, and all of them can be run in parallel, theconcurrency control may execute the pre-execution operations ofannotator #1 in parallel with the pre-execution operations of annotator#3 first and then the pre-execution operations of annotator #2 areexecuted in parallel with the pre-execution operations of annotator #4.Of course, this is in the steady-state when each annotator has enoughdata to run.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIGS. 1-3 are directed to describing an example Question/Answer,Question and Answer, or Question Answering (QA) system, methodology, andcomputer program product with which the mechanisms of the illustrativeembodiments may be implemented. As will be discussed in greater detailhereafter, the illustrative embodiments may be integrated in, and mayaugment and extend the functionality of, these QA mechanisms with regardto the execution of the annotators/aggregates of the QA system. Morespecifically, the illustrative embodiments provide mechanisms forscheduling the execution of pre-execution operations ofannotators/aggregates so at to optimize the timing and resource usage ofannotators/aggregates when performing functions for hypothesisgeneration, evidence evaluation, and the like.

Since the mechanisms of the illustrative embodiments operate to improvethe operation of a QA system, it is important to first have anunderstanding of how question and answer generation in a QA system maybe implemented before describing how the mechanisms of the illustrativeembodiments are integrated in and augment such QA systems. It should beappreciated that the QA mechanisms described in FIGS. 1-3 are onlyexamples and are not intended to state or imply any limitation withregard to the type of QA mechanisms with which the illustrativeembodiments may be implemented. Many modifications to the example QAsystem shown in FIGS. 1-3 may be implemented in various embodiments ofthe present invention without departing from the spirit and scope of thepresent invention.

QA mechanisms operate by accessing information from a corpus of data orinformation (also referred to as a corpus of content), analyzing it, andthen generating answer results based on the analysis of this data.Accessing information from a corpus of data typically includes: adatabase query that answers questions about what is in a collection ofstructured records, and a search that delivers a collection of documentlinks in response to a query against a collection of unstructured data(text, markup language, etc.). Conventional question answering systemsare capable of generating answers based on the corpus of data and theinput question, verifying answers to a collection of questions for thecorpus of data, correcting errors in digital text using a corpus ofdata, and selecting answers to questions from a pool of potentialanswers, i.e. candidate answers.

Content creators, such as article authors, electronic document creators,web page authors, document database creators, and the like, maydetermine use cases for products, solutions, and services described insuch content before writing their content. Consequently, the contentcreators may know what questions the content is intended to answer in aparticular topic addressed by the content. Categorizing the questions,such as in terms of roles, type of information, tasks, or the like,associated with the question, in each document of a corpus of data mayallow the QA system to more quickly and efficiently identify documentscontaining content related to a specific query. The content may alsoanswer other questions that the content creator did not contemplate thatmay be useful to content users. The questions and answers may beverified by the content creator to be contained in the content for agiven document. These capabilities contribute to improved accuracy,system performance, machine learning, and confidence of the QA system.Content creators, automated tools, or the like, may annotate orotherwise generate metadata for providing information useable by the QAsystem to identify these question and answer attributes of the content.

Operating on such content, the QA system generates answers for inputquestions using a plurality of intensive analysis mechanisms whichevaluate the content to identify the most probable answers, i.e.candidate answers, for the input question. The illustrative embodimentsleverage the work already done by the QA system to reduce thecomputation time and resource cost for subsequent processing ofquestions that are similar to questions already processed by the QAsystem.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer creation (QA) system 100 in a computer network 102. Oneexample of a question/answer generation which may be used in conjunctionwith the principles described herein is described in U.S. PatentApplication Publication No. 2011/0125734, which is herein incorporatedby reference in its entirety. The QA system 100 may be implemented onone or more computing devices 104 (comprising one or more processors andone or more memories, and potentially any other computing deviceelements generally known in the art including buses, storage devices,communication interfaces, and the like) connected to the computernetwork 102. The network 102 may include multiple computing devices 104in communication with each other and with other devices or componentsvia one or more wired and/or wireless data communication links, whereeach communication link may comprise one or more of wires, routers,switches, transmitters, receivers, or the like. The QA system 100 andnetwork 102 may enable question/answer (QA) generation functionality forone or more QA system users via their respective computing devices110-112. Other embodiments of the QA system 100 may be used withcomponents, systems, sub-systems, and/or devices other than those thatare depicted herein.

The QA system 100 may be configured to implement a QA system pipeline108 that receive inputs from various sources. For example, the QA system100 may receive input from the network 102, a corpus of electronicdocuments 106, QA system users, or other data and other possible sourcesof input. In one embodiment, some or all of the inputs to the QA system100 may be routed through the network 102. The various computing devices104 on the network 102 may include access points for content creatorsand QA system users. Some of the computing devices 104 may includedevices for a database storing the corpus of data 106 (which is shown asa separate entity in FIG. 1 for illustrative purposes only). Portions ofthe corpus of data 106 may also be provided on one or more other networkattached storage devices, in one or more databases, or other computingdevices not explicitly shown in FIG. 1. The network 102 may includelocal network connections and remote connections in various embodiments,such that the QA system 100 may operate in environments of any size,including local and global, e.g., the Internet.

In one embodiment, the content creator creates content in a document ofthe corpus of data 106 for use as part of a corpus of data with the QAsystem 100. The document may include any file, text, article, or sourceof data for use in the QA system 100. QA system users may access the QAsystem 100 via a network connection or an Internet connection to thenetwork 102, and may input questions to the QA system 100 that may beanswered by the content in the corpus of data 106. In one embodiment,the questions may be formed using natural language. The QA system 100may interpret the question and provide a response to the QA system user,e.g., QA system user 110, containing one or more answers to thequestion. In some embodiments, the QA system 100 may provide a responseto users in a ranked list of candidate answers.

The QA system 100 implements a QA system pipeline 108 which comprises aplurality of stages for processing an input question, the corpus of data106, and generating answers for the input question based on theprocessing of the corpus of data 106. The QA system pipeline 108 will bedescribed in greater detail hereafter with regard to FIG. 3.

In some illustrative embodiments, the QA system 100 may be the Watson™QA system available from International Business Machines Corporation ofArmonk, N.Y., which is augmented with the mechanisms of the illustrativeembodiments described hereafter. The Watson™ QA system may receive aninput question which it then parses to extract the major features of thequestion, that in turn are then used to formulate queries that areapplied to the corpus of data. Based on the application of the queriesto the corpus of data, a set of hypotheses, or candidate answers to theinput question, are generated by looking across the corpus of data forportions of the corpus of data that have some potential for containing avaluable response to the input question.

The Watson™ QA system then performs deep analysis on the language of theinput question and the language used in each of the portions of thecorpus of data found during the application of the queries using avariety of reasoning algorithms. There may be hundreds or even thousandsof reasoning algorithms applied, each of which performs differentanalysis, e.g., comparisons, and generates a score. For example, somereasoning algorithms may look at the matching of terms and synonymswithin the language of the input question and the found portions of thecorpus of data. Other reasoning algorithms may look at temporal orspatial features in the language, while others may evaluate the sourceof the portion of the corpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the Watson™ QA system has regarding the evidence that the potentialresponse, i.e. candidate answer, is inferred by the question. Thisprocess may be repeated for each of the candidate answers until theWatson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. More information aboutthe Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developer Works, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments may be implemented. Dataprocessing system 200 is an example of a computer, such as server 104 orclient 110 in FIG. 1, in which computer usable code or instructionsimplementing the processes for illustrative embodiments of the presentinvention may be located. In one illustrative embodiment, FIG. 2represents a server computing device, such as a server 104, which, whichimplements a QA system 100 and QA system pipeline 108 augmented toinclude the additional mechanisms of the illustrative embodimentsdescribed hereafter.

In the depicted example, data processing system 200 employs a hubarchitecture including north bridge and memory controller hub (NB/MCH)202 and south bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 areconnected to NB/MCH 202. Graphics processor 210 may be connected toNB/MCH 202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connectsto SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive230, universal serial bus (USB) ports and other communication ports 232,and PCI/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus240. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbasic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD226 and CD-ROM drive 230 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 236 may be connected to SB/ICH 204.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within the dataprocessing system 200 in FIG. 2. As a client, the operating system maybe a commercially available operating system such as Microsoft® Windows7®. An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 200.

As a server, data processing system 200 may be, for example, an IBM®eServer™ System p® computer system, running the Advanced InteractiveExecutive (AIX®) operating system or the LINUX® operating system. Dataprocessing system 200 may be a symmetric multiprocessor (SMP) systemincluding a plurality of processors in processing unit 206.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 226, and may be loaded into main memory 208 for execution byprocessing unit 206. The processes for illustrative embodiments of thepresent invention may be performed by processing unit 206 using computerusable program code, which may be located in a memory such as, forexample, main memory 208, ROM 224, or in one or more peripheral devices226 and 230, for example.

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, may becomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 222 or network adapter 212 of FIG. 2, may include one or moredevices used to transmit and receive data. A memory may be, for example,main memory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG.2.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIGS. 1 and 2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS. 1and 2. Also, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system, other than the SMPsystem mentioned previously, without departing from the spirit and scopeof the present invention.

Moreover, the data processing system 200 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 200 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 200 may be any known or later developed dataprocessing system without architectural limitation.

FIG. 3 illustrates a QA system pipeline for processing an input questionin accordance with one illustrative embodiment. The QA system pipelineof FIG. 3 may be implemented, for example, as QA system pipeline 108 ofQA system 100 in FIG. 1. It should be appreciated that the stages of theQA system pipeline shown in FIG. 3 may be implemented as one or moresoftware engines, components, or the like, which are configured withlogic for implementing the functionality attributed to the particularstage. Each stage may be implemented using one or more of such softwareengines, components or the like. The software engines, components, etc.may be executed on one or more processors of one or more data processingsystems or devices and may utilize or operate on data stored in one ormore data storage devices, memories, or the like, on one or more of thedata processing systems. The QA system pipeline of FIG. 3 may beaugmented, for example, in one or more of the stages to implement theimproved mechanism of the illustrative embodiments described hereafter,additional stages may be provided to implement the improved mechanism,or separate logic from the pipeline 300 may be provided for interfacingwith the pipeline 300 and implementing the improved functionality andoperations of the illustrative embodiments

As shown in FIG. 3, the QA system pipeline 300 comprises a plurality ofstages 310-380 through which the QA system operates to analyze an inputquestion and generate a final response. In an initial question inputstage 310, the QA system receives an input question that is presented ina natural language format. That is, a user may input, via a userinterface, an input question for which the user wishes to obtain ananswer, e.g., “Who are Washington's closest advisors?” In response toreceiving the input question, the next stage of the QA system pipeline500, i.e. the question and topic analysis stage 320, parses the inputquestion using natural language processing (NLP) techniques to extractmajor features from the input question, classify the major featuresaccording to types, e.g., names, dates, or any of a plethora of otherdefined topics. For example, in the example question above, the term“who” may be associated with a topic for “persons” indicating that theidentity of a person is being sought, “Washington” may be identified asa proper name of a person with which the question is associated,“closest” may be identified as a word indicative of proximity orrelationship, and “advisors” may be indicative of a noun or otherlanguage topic.

The identified major features may then be used during the questiondecomposition stage 330 to decompose the question into one or morequeries that may be applied to the corpora of data/information 345 inorder to generate one or more hypotheses. The queries may be generatedin any known or later developed query language, such as the StructureQuery Language (SQL), or the like. The queries may be applied to one ormore databases storing information about the electronic texts,documents, articles, websites, and the like, that make up the corpora ofdata/information 345. That is, these various sources themselves,different collections of sources, and the like, may represent adifferent corpus 347 within the corpora 345. There may be differentcorpora 347 defined for different collections of documents based onvarious criteria depending upon the particular implementation. Forexample, different corpora may be established for different topics,subject matter categories, sources of information, or the like. As oneexample, a first corpus may be associated with healthcare documentswhile a second corpus may be associated with financial documents.Alternatively, one corpus may be documents published by the U.S.Department of Energy while another corpus may be IBM Redbooks documents.Any collection of content having some similar attribute may beconsidered to be a corpus 347 within the corpora 345.

The queries may be applied to one or more databases storing informationabout the electronic texts, documents, articles, websites, and the like,that make up the corpus of data/information, e.g., the corpus of data106 in FIG. 1. The queries being applied to the corpus ofdata/information at the hypothesis generation stage 340 to generateresults identifying potential hypotheses for answering the inputquestion which can be evaluated. That is, the application of the queriesresults in the extraction of portions of the corpus of data/informationmatching the criteria of the particular query. These portions of thecorpus may then be analyzed and used, during the hypothesis generationstage 340, to generate hypotheses for answering the input question.These hypotheses are also referred to herein as “candidate answers” forthe input question. For any input question, at this stage 340, there maybe hundreds of hypotheses or candidate answers generated that may needto be evaluated.

The QA system pipeline 300, in stage 350, then performs a deep analysisand comparison of the language of the input question and the language ofeach hypothesis or “candidate answer” as well as performs evidencescoring to evaluate the likelihood that the particular hypothesis is acorrect answer for the input question. As mentioned above, this mayinvolve using a plurality of reasoning algorithms, each performing aseparate type of analysis of the language of the input question and/orcontent of the corpus that provides evidence in support of, or not, ofthe hypothesis. Each reasoning algorithm generates a score based on theanalysis it performs which indicates a measure of relevance of theindividual portions of the corpus of data/information extracted byapplication of the queries as well as a measure of the correctness ofthe corresponding hypothesis, i.e. a measure of confidence in thehypothesis.

In the synthesis stage 360, the large number of relevance scoresgenerated by the various reasoning algorithms may be synthesized intoconfidence scores for the various hypotheses. This process may involveapplying weights to the various scores, where the weights have beendetermined through training of the statistical model employed by the QAsystem and/or dynamically updated, as described hereafter. The weightedscores may be processed in accordance with a statistical model generatedthrough training of the QA system that identifies a manner by whichthese scores may be combined to generate a confidence score or measurefor the individual hypotheses or candidate answers. This confidencescore or measure summarizes the level of confidence that the QA systemhas about the evidence that the candidate answer is inferred by theinput question, i.e. that the candidate answer is the correct answer forthe input question.

The resulting confidence scores or measures are processed by a finalconfidence merging and ranking stage 370 which may compare theconfidence scores and measures, compare them against predeterminedthresholds, or perform any other analysis on the confidence scores todetermine which hypotheses/candidate answers are the most likely to bethe answer to the input question. The hypotheses/candidate answers maybe ranked according to these comparisons to generate a ranked listing ofhypotheses/candidate answers (hereafter simply referred to as “candidateanswers”). From the ranked listing of candidate answers, at stage 380, afinal answer and confidence score, or final set of candidate answers andconfidence scores, may be generated and output to the submitter of theoriginal input question.

As shown in FIG. 3, in accordance the illustrative embodiments, as partof the question and topic analysis stage 320, annotators 325 areutilized to perform the feature extraction and annotation. That is, theannotations identify the types of features extracted from the inputquestion, e.g., nouns, verbs, places, names, values, or domain specifictypes. As mentioned above, the annotators 325 may comprise annotators ofvarious domains including generic all inclusive domains, specializeddomains for particular industries, topics, and the like, or anygranularity of domain between general and specialized. The annotationsmay be stored as separate sets of annotations 327, associated with theinput question, generated by each annotator 325. The annotations 327 maybe stored as separate data structures from the input question or may bestored as metadata of the input question. Thus, a first set ofannotations may comprise annotations generated by an annotatorconfigured to annotate financial terms in input questions. A second setof annotations may comprise annotations generated by an annotatorconfigured to perform English language parts of speech identification.It should be appreciated that an annotator may actually not generate anyannotations at all if the input question does not comprise terms orpatterns of content matching those terms and patterns of content forwhich the annotator is configured to identify and annotate.

In one illustrative embodiment, the annotations may comprise parts ofspeech, definitions and/or types of terms, and expected contextinformation. The parts of speech portion of an annotation identifieswhether the term, phrase, or portion of text is a noun, verb,preposition, prepositional phrase, adverb, adjective, or other part ofspeech of a particular language. The definitions annotations provide thedefinitions of terms or patterns of terms from the viewpoint of thedomain for which the annotation is configured. Thus, in one annotator aterm may have a first definition, and in another annotator, the sameterm may have a second, different definition. The same is true of typedesignations, where the type may be domain specific. Furthermore, thetype may designate such things as the focus of the input question, thelexical answer type (LAT) of the question, and other structuralannotations associated with the terms, pattern of terms, or the like.The expected context information annotations comprise the expecteddomain of the question determined from analysis of the various parts ofthe question, e.g., that the question is determined to be in thefinancial, medical, oncology, English grammar, or other type of domain(subject matter area) that is expected to be the most likely area toprovide a correct answer for the question. Various types of annotatorsmay be used when analyzing the input question and/or the corpus.

For example, consider the statement “John should have short AMG.” With ageneral purpose annotator, this statement may be parsed and annotated inthe following manner (with annotations indicated in parenthesis): “John(Noun) should have (Verb) short (Noun) AMG (Unknown, Noun)”. A financialdomain based annotator may recognize the term “short” to be averb/adverb part of speech indicating the operation of “sellingcommodities high, buy low to return to holdings” or “sells borrowedstocks.” Furthermore, the financial domain annotator may be configuredto identify particular types of terms corresponding to the financialindustry, such as stock names, mutual fund names, stock exchanges, andthe like. Thus, the financial domain annotator may annotate the samestatement as follows: “John (Noun) should have (Verb) short (Verb) AMG(Noun, stock name).” Further annotators may be used to determine thedomain of an input question of document/portion of content so as todetermine which set of annotators to utilized or prioritize, e.g.,generic annotator versus domain specific annotator.

It should be appreciated that while the illustrative embodiments aredescribed with reference to annotators used in the question and topicanalysis stage 320 of the QA system pipeline 300, the illustrativeembodiments are not limited to such and may operate with regard toannotators in any stage of the QA system pipeline 300. For example,annotators may be utilized in the hypothesis generation stage 340,evidence scoring stage 350, or the like, and may have their operationoptimized using the mechanisms of the illustrative embodiments. Anyannotator or group of annotators (an “aggregate”) of a QA systempipeline 300 may be optimized using the mechanisms of the illustrativeembodiments without departing from the spirit and scope of theillustrative embodiments.

As mentioned above, the illustrative embodiments provide mechanisms forutilizing an open queuing network to approximate and model the responsetime associated with an annotator or aggregate of annotators (referredto herein as an “aggregate”). An “open queuing network” is a network ofqueues in which the jobs may be received from outside of the queuingnetwork and the number and rate of the job input is variable. The queuesof an open queuing network are characterized by their service rates forprocessing jobs, with the throughput of the open queuing network being acombination of the service rates of the individual queues.

Referring again to FIG. 3, the mechanisms of the illustrativeembodiments may be implemented as an annotator modeling and schedulingengine 390 which may operate in conjunction with the annotators 325(which may also comprise aggregates as well) of the question and topicanalysis stage 320, or other stage in other possible implementations.The annotator modeling and scheduling engine 390 may comprise logic,e.g., modeling logic 394 and scheduling logic 396, implemented inhardware, software executed on hardware, or a combination of hardwareand software executed on hardware, for modeling the annotators 325 as atandem queue in which each annotator/aggregate is a queue noderepresenting a separate open queue in the tandem queue model. Themodeling logic 394 may further determine response times andpre-execution start intervals for the various nodes in the model 392which may then be used by the scheduling logic 396 to determineappropriate scheduling of pre-execution operations ofannotators/aggregates based on the modeled response time, and theconfiguration of downstream annotators/aggregates, so as to optimize theoverall response time for the annotators/aggregates of the question andtopic analysis stage 320.

With the mechanisms of the illustrative embodiment, each annotator oraggregate 325 is defined as a sub-system and has its configurationdesignated, such as in annotator/aggregate configuration data structure397, as to whether they have one or more pre-execution operations thatmay be executed with the results of the pre-execution operations beingstored/cached for use by the annotator/aggregate 325. The entire groupof subsystems, i.e. the group of annotators and aggregates, arerepresented as a tandem open queue network in a model 392 with eachsubsystem being a queue in the tandem open queue network. In some cases,a subsystem may be large enough, i.e. an aggregate may have a largeenough number of annotators, that it may be modeled as a tandem queue initself. Thus, a nested tandem queue networking model may be generated inthis manner.

The model 392 of the annotators/aggregates is generated and simulated bythe modeling logic 396 using simulated job workloads to define anddetermine the average response time for each subsystem, i.e. the“effective response time.” The effective response time of a subsystem(annotator/aggregate) in the model 392 is used to determine when thepre-execution operations of the next subsystem (annotator/aggregate) inthe model 392 can begin executing so as to optimize the timing of theexecution of the pre-execution operation and the subsequent subsystem.In determining when the pre-execution operations of the next subsystemmay begin execution, a calculation of the current execution time minusthe effective response time of the previous subsystem is set equal tothe pre-execution start interval.

This process may be performed for each subsystem of the model 392 togenerate pre-execution start intervals for each subsystem of the model392. Moreover, this process may be performed for various types ofworkloads, e.g., workloads for different domains. The result is a set ofscheduling parameters that may be stored in a scheduling data structure398 for use in scheduling the operation of annotators/aggregates whenperforming operations during runtime. The scheduling data structure 398may store, for example, for each of the annotators/aggregates, one ormore scheduling parameters specifying effective response time of theannotator/aggregate, pre-execution start interval for theannotator/aggregate, or the like, and may store multiple sets of suchparameters keyed to other characteristics of input to theannotator/aggregate, e.g., specified domain of the input data (such asthe specified domain of the input question, domain of the document orcorpora being processed, or the like).

This scheduling data structure 398 may be accessed by scheduling logic396 of the annotator modeling and scheduling engine 390 when schedulingthe execution of the annotators/aggregates for a particular inputquestion and/or corpora. That is, during the operation of the questionand topic analysis stage 320, certain annotators/aggregates may alwaysbe executed, e.g., generic annotators/aggregates, while otherannotators/aggregates may be domain dependent or dependent upon resultsgenerated by other annotators. In either case, the scheduling of theannotators/aggregates may be performed based on the determined responsetimes and pre-execution start intervals such that if a particularannotator/aggregate is to be scheduled for execution, the pre-executionstart interval for the particular annotator/aggregate may be used toschedule the execution of the pre-execution operations for theannotator/aggregate. Thus, when the annotator/aggregate is scheduled toexecute, the pre-execution operations have been already completed. Suchpre-execution operations may be performed in parallel with theoperations of other annotators/aggregates since these pre-executionoperations are non-dependent upon the results generated by the earlierexecuted annotators/aggregates.

Hence, during runtime operation, the pre-execution start intervals maybe used for scheduling the pre-execution operations of theannotators/aggregates, e.g., pre-loading, into memory, of a corpora uponwhich the annotator will operate, pre-loading into memory other dataused by the annotator/aggregate such as type maps and the like, or anyother operation that is a not dependent upon other annotators/aggregateoutput results. In executing the pre-execution operations, thepre-execution operation checks for pre-execution input data and startsthe pre-execution operation in response to the pre-execution input databeing available. If the pre-execution input data is not available, thenoperation continues to check until the pre-execution input data isavailable at which time the pre-execution operation is initiated. Insome embodiments, pre-execution input data may be placed in a queuestructure (not shown) from which the pre-execution input data is loadedand annotators/aggregates are executed on demand but not before the mostoptimal pre-execution start interval. Once the pre-execution operationis executed, the resulting intermediate data is stored/cached for use bythe annotator/aggregate when the annotator/aggregate is fully executed.The annotator/aggregate is then executed fully in accordance with itsnormal operation.

As mentioned above, the system of annotators/aggregates is modeled as atandem open queuing network. FIG. 4A illustrates one example of a tandemopen queuing network model of a system of annotators in accordance withone illustrative embodiment. As shown in FIG. 4A, eachannotator/aggregate of a QA system pipeline has correspondingconfiguration data as input to the modeling logic which uses thisconfiguration data to model each annotator/aggregate as a subsystem402-406 in the model. The configuration data specifies whether theannotator/aggregate has any pre-execution operations that areindependent of other annotators/aggregates and can be executed such thatthe results of the pre-execution operations may be stored/cached. Thus,it is known for each annotator/aggregate whether the annotator/aggregatehas any pre-execution operations that may be optimized by the mechanismsof the illustrative embodiments.

As shown in FIG. 4A, the subsystems 402-406 are modeled as a tandem openqueue network 410 where each subsystem 402-406 is represented as aseparate queue 412-416 in the tandem open queue network 410. A model isdeveloped to define and determine the average response time for eachsubsystem, i.e. the effective response time. In generating this modelfor determining the average response time for each subsystem, theend-to-end mean or average response time of the system as a whole is thesum of the individual subsystem response times. For a single equivalentqueue, the average response time may be calculated using Little's Law inthe following manner. Denote E[R] to be the average response time, oreffective response time, where R is the response time variable. Denoteμ_(eq) to be the equivalent service rate (e.g., in jobs/sec) and λ to bethe arrival rate (e.g., in jobs/sec). The effective response time E[R]is equal to 1/(μ_(eq)−λ). The values to be used in these relations maybe obtained, for example, by profiling each annotator and the QA systempipeline as a whole, and measuring the values of these variables overtime. Simulations may be used when no empirical data is available.Branch probabilities for branching from one annotator operation toanother, and which affect the arrival rate into each annotator which inturn affects the response time, can also be measured over time orsimulated by looking at how data routes among the different annotators,such as by tagging the data and tracking the data routes based on thetags.

The effective response time calculated using this model of the systemand each of the subsystems is used to calculate when the pre-executionof a next subsystem (annotator/aggregate) should begin to optimize theexecution of the pre-execution operations for this next subsystem. Asmentioned above, in one illustrative embodiment, this pre-executionstart interval may be calculated as the difference between the currentexecution time, or the start time of the next subsystem, and theeffective response time of the previous subsystem.

With reference again to FIG. 4A, as shown each of subsystems 402-406 aremodeled as a separate queue 412-416. Each of these separate subsystems402-406 may further be modeled as an open queuing network as shown inFIG. 4B. In FIG. 4B, a subsystem, e.g., subsystem 412 in FIG. 4A, maycomprise a plurality of annotators (or “stations”), each represented bya single queue structure 422-426 having corresponding service ratesμ_(T), μ_(O), μ_(S), etc. which may be specified, for example, asjobs/second or another suitable service rate metric. The arrival rate ofjobs into this subsystem 412 is represented by the value λ₁, which maybe in jobs/sec or other suitable arrival rate metric, for example. Thearrival rate λ₂ of a next subsystem, e.g., subsystem 414 in FIG. 4A, inthe model is equal to the output rate of the present subsystem 412.Branching probabilities P_(TO), P_(OT), P_(TF), P_(OS), P_(OF) representthe probability that the operation of the station will result in abranch of execution to a particular other station of the subsystem. Thebranching probabilities can be experimentally measured over time byobserving how the data routes across different stations. The subscriptsof the branching probabilities indicate the stations. For example, theprobability that execution will branch to station S from station O isgiven by P_(OS). These branching probabilities affect μ_(eq) as well asthe overall response time.

The subsystem as a whole may be represented as a single equivalent queuerepresentation as shown in FIG. 4C. That is, jobs arrive into the singleequivalent queue at an arrival rate of λ₁ and the single equivalentqueue has a service rate of μ_(eq1) which results in the output of thesubsystem being provided at time T1 which is equal to the arrival rateλ₂ at the next subsystem.

Thus, as shown in FIG. 4B, a tandem open queue model of a subsystem maybe utilized to represent each of the subsystems in the model of theannotators/aggregates which may then be represented as a singleequivalent queue representation as shown in FIG. 4C. Each model of thesubsystem, such as shown in FIG. 4C, may be combined to generate atandem open queue model of the system as a whole, as shown in FIG. 4A.

Furthermore, as shown in FIG. 4D, the overall system model may berepresented as a discrete time Markov chain (DTMC) model in which thestates of the DTMC are individual subsystems and the transitions betweenthe states, or subsystems, are represented by the branchingprobabilities. The DTMC model shown in FIG. 4D is a DTMC model forprocessing a single job through the example subsystem shown in FIG. 4B.DTMC models are generally known in the art as being mathematical systemsthat undergo transitions from one state to another in a state spaceunder a random process characterized in that the next state depends onlyon the current state and not on the sequence of events that preceded it.While DTMCs may be generally known in the art, the specificimplementation of a DTMC to represent a system of annotators/aggregatesof a QA system with each state of the DTMC being a subsystem(annotator/aggregate) of a QA system and transitions between thesesubsystems being represented by branching probabilities has not beenimplemented prior to the present invention.

By modeling the annotators/aggregates as subsystems in which eachsubsystem is represented by a queue in a tandem open queue network(which represents the system of annotators/aggregates as a whole) theeffective response times of each of the annotators/aggregates may becalculated and used to determine the pre-execution start intervals forsubsequent annotators/aggregates based on the effective response timesof the prior annotator/aggregate. Thus, if it is known that aggregator Bfollows aggregator A in processing a particular job, and it is knownthat aggregated A has an effective response time of 10 seconds whileaggregator B will start execution at 15 seconds, then if aggregator Bhas any pre-execution operations that may be performed independentlywith the results stored/cached for use by aggregator B, thesepre-execution operations may be scheduled to being 5 seconds earlierthan the execution of aggregator B. Similarly, if it is known thataggregator C then begins execution at a 30 second time point, andaggregator B has an effective response time of 10 seconds, thenaggregator C may likewise have its pre-execution operations scheduled tobeing execution 5 seconds earlier than the start time of aggregator C,i.e. aggregator B starts execution at 15 seconds, its effective responsetime is 10 seconds which means that it will complete execution at 25seconds giving a 5 second window before aggregator C is scheduled toexecute and in which the pre-execution operations may be executing inpreparation for the start of aggregator C.

As mentioned above, the model of the annotators/aggregators may be usedto generate effective response times for the variousannotators/aggregators as well as pre-execution start intervals for theannotators/aggregators which may be stored in a scheduling datastructure. The scheduling data structure may be accessed byannotator/aggregate scheduling logic to establish a schedule ofexecution times of the annotators/aggregates of the various stages ofthe QA system pipeline including scheduling pre-execution operations toexecute at the determined pre-execution start intervals prior to theircorresponding annotator/aggregator start time. As a result, thepre-execution operations are performed earlier in time in preparation ofthe execution of the corresponding annotator/aggregator, therebyreducing wasted processor cycles and optimizing the use of systemresources.

As an additional benefit, the calculated effective response times may beused to identify bottlenecks within the QA system pipelines'annotators/aggregates. That is, if a particular annotator/aggregate'seffective response time is larger than a predetermined threshold value,or is more than a predetermined percentage greater than otherannotators/aggregates used by the QA system pipeline, this may beindicative of a bottleneck in the processing of jobs through theannotators/aggregates. Logic may be provided in the annotator modelingand scheduling engine 390 for identifying such bottlenecks by comparingthe calculated effective response times to one or more predeterminedthresholds, or to effective response times of otherannotators/aggregates, to an average of effective response times ofannotators/aggregates, or the like. If a potential bottleneck isidentified through such a comparison, e.g. an effective response timegreater than the threshold, an effective response time that is more thana predetermined percentage higher than other annotators/aggregates orthe average of effective response times of other annotators/aggregates,then this bottleneck may be flagged or otherwise identified in thescheduling data structure by storing an indicator of such a bottleneckin association with an entry for the annotator/aggregate and anotification may be sent to an administrator identifying the potentialbottleneck.

Thus, the illustrative embodiments provide mechanisms for optimizing thescheduling of pre-execution operations of annotators/aggregates so as totake into consideration the effective response times of theannotators/aggregates and their corresponding precedingannotators/aggregates. The pre-execution operations may be scheduledsuch that their operations are completed prior to the fully execution ofthe corresponding annotator/aggregate thereby reducing the time requiredfor the annotators/aggregates to complete execution. As a result, theoverall performance of the annotators/aggregates and the QA systempipeline as a whole is improved.

FIG. 5 is a flowchart outlining an example operation for determining theeffective response time and pre-execution start interval forannotators/aggregates in accordance with one illustrative embodiment. Asshown in FIG. 5, the operation starts with receiving annotator/aggregateconfiguration information (step 510). The annotator/aggregateconfiguration information is used to generate subsystem representationsfor each of the annotators/aggregates with annotators/aggregates havingpre-execution operations that can be optimized being identified (step520). A tandem open queue network model of a system ofannotators/aggregates is generated based on the subsystemrepresentations in which each subsystem is represented as a separatequeue in the model (step 530). The effective response time of eachsubsystem in the model is calculated based on the arrival rate into thesubsystem and the service rate of the subsystem (step 540). Based on theeffective response times and the configuration of theannotators/aggregates of the system, the pre-execution start intervalsfor annotators/aggregates in the system that have pre-executionoperations that may be executed independently of otherannotators/aggregates are calculated (step 550). The resulting effectiveresponse times, pre-execution start intervals, and configuration dataare stored in a scheduling data structure for use in schedulingannotators/aggregates during runtime operation (step 560). The operationthen terminates.

FIG. 6 is a flowchart outlining an example operation for scheduling theoperation of annotators/aggregates in accordance with one illustrativeembodiment. As shown in FIG. 6, the operation starts by receiving a jobto be processed by a system of annotators/aggregates (step 610). Acorresponding system of annotators/aggregates is identified for theparticular job, e.g., generic set of annotators/aggregates, domainspecific set of annotators/aggregates based on the domain of the job tobe performed, a combination of generic and domain specificannotators/aggregates, or the like (step 620). The correspondingscheduling data for the set of annotators/aggregates is retrieved from ascheduling data structure (step 630). The effective response times andpre-execution start intervals for the annotators/aggregates in the setof annotators/aggregates are used to schedule the execution of theannotators/aggregates and any pre-execution operations for theannotators/aggregates (step 640). The job is then processed by theannotators/aggregates in accordance with the specified schedule (step650) and the operation terminates.

It should be noted that while the illustrative embodiments have beendescribed in terms of particular models and calculations, theillustrative embodiments are not limited to such. Rather, any model andset of calculations that permit the determination of a pre-executionstart interval for pre-execution operations of annotators/aggregatorsmay be used without departing from the spirit and scope of theillustrative embodiments. That is, for example, rather than using atandem open queue network to represent the system ofannotators/aggregates, a different model may be utilized that permitsthe calculation of pre-execution start intervals. Moreover, rather thanusing a DTMC model to represent a system of annotators/aggregates, otherstate diagrams or models may be utilized as well. Furthermore, otherequations and calculations for calculating the effective response timesof the various annotators/aggregates and corresponding pre-executionstart intervals may be used as well. Thus, the illustrative embodimentsdescribed herein should not be construed as limiting on the claims setforth hereafter.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers. Network adapters mayalso be coupled to the system to enable the data processing system tobecome coupled to other data processing systems or remote printers orstorage devices through intervening private or public networks. Modems,cable modems and Ethernet cards are just a few of the currentlyavailable types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to best explain theprinciples of the invention, the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A method, in a data processing system comprisinga processor and a memory, for scheduling execution of pre-executionoperations of an annotator of a question and answer (QA) systempipeline, the method comprising: using, by the data processing system, amodel to represent a system of annotators of the QA system pipeline,wherein the model represents each annotator in the system of annotatorsas a node having one or more performance parameters for indicating aperformance of an execution of an annotator corresponding to the node,wherein each annotator in the s stem of annotators is a program thattakes a portion of unstructured input text, extracts structuredinformation from the portion of the unstructured input text, andgenerates annotations or metadata that are attached by the annotator toa source of the unstructured input text, wherein, for each node in themodel, the one or more performance parameters corresponding to the nodecomprise an arrival rate parameter and a service rate parameter of theannotator associated with the node, wherein the arrival rate parameterindicates a number of jobs arriving in the node per second, and whereinthe service rate parameter indicates a number of jobs being serviced bythe node per second; determining, by the data processing system, foreach annotator in a set of annotators of the system of annotators, aneffective response time for the annotator based on the one or moreperformance parameters; calculating, by the data processing system, apre-execution start interval for a first annotator based on an effectiveresponse time of a second annotator, wherein execution of the firstannotator is sequentially after execution of the second annotator; andscheduling, by the data processing system, execution of pre-executionoperations associated with the first annotator based on the calculatedpre-execution start interval for the first annotator.
 2. The method ofclaim 1, wherein at least one node in the model represents a pluralityof annotators comprising an aggregate annotator.
 3. The method of claim1, wherein the system is a tandem open queuing network in which eachnode in the model is modeled as a queue in the tandem open queuingnetwork.
 4. The method of claim 1, wherein each annotator of the QAsystem pipeline is defined, in a data structure, as a sub-system of theQA system pipeline and is designated as either having or not having oneor more non-dependent pre-execution operations that may be executed withthe results of the one or more non-dependent pre-execution operationsbeing stored/cached for use by the annotator, and wherein schedulingexecution of pre-execution operations associated with the firstannotator is performed in response to the data structure indicating thatthe pre-execution operations associated with the first annotator arenon-dependent pre-execution operations.
 5. The method of claim 1,wherein calculating the pre-execution start interval for the firstannotator based on an effective response time of the second annotatorcomprises calculating the pre-execution start interval based on adifference of a current execution time and an effective response time ofthe second annotator.
 6. The method of claim 1, wherein the schedulinggenerates a scheduling data structure, and wherein the method furthercomprises: receiving a job for processing by the QA system pipeline;selecting a set of annotators in the system of annotators to execute thejob, the set of annotators comprising the first annotator and the secondannotator; scheduling the set of annotators, including the pre-executionoperation of the first annotator, based on the scheduling datastructure; and processing the job based on the scheduling of the set ofannotators.